In this study, a hybrid stochastic model (BScA model) using Bayesian approach and scenario analysis to forecast long term energy demand is developed. The main objective of this study is to design and develop a model for energy analysis in demand side and describe the energy saving and GHG reduction potential on the other. For this, total energy demand is selected as the response variable and primary energy production, population, GDP and natural gas and gasoline prices are chosen as covariates. Also, Political drivers, economic drivers, social-environmental and technological drivers are the key driving forces for scenario development. After interview and ranking the drivers, we have built scenario matrixes and reducing them upon strengths, weaknesses, opportunities and how the energy system perspective in each of the scenarios develop. Results show that primary energy production and population growth have positive impact on energy demand. And Energy consumption would decrease with energy price increase. And, economic development would rise energy demand. Also, the total potential for energy saving is equal to 3663 MBOE in duration of 2016–2040. Results demonstrate the energy intensity (EI) will be 2.12 MBOE/Million Rials in 2040 if energy saving solutions are taken. And, the carbon emission will reduce about 32% in 2040.

在这项研究中，开发了一种使用贝叶斯方法和情景分析来预测长期能源需求的混合随机模型（BScA模型）。这项研究的主要目的是设计和开发需求侧能源分析模型，并在另一方面描述节能和减少温室气体的潜力。为此，选择总能源需求作为响应变量，并选择一次能源生产，人口，GDP，天然气和汽油价格作为协变量。同样，政治驱动力，经济驱动力，社会环境和技术驱动力是情景发展的关键驱动力。在对司机进行访谈和排名之后，我们建立了情景矩阵，并根据优势，劣势，机会以及每种情景中能源系统的视角如何发展对它们进行了简化。结果表明，一次能源生产和人口增长对能源需求产生积极影响。随着能源价格上涨，能源消耗将减少。并且，经济发展将增加能源需求。此外，在2016-2040年间，节能的总潜力等于3663 MBOE。结果表明，如果采用节能解决方案，那么到2040年，能源强度（EI）将为2.12 MBOE /百万里亚尔。并且，到2040年，碳排放量将减少约32％。如果采用节能解决方案，则到2040年将达到12 MBOE /百万里亚尔。并且，到2040年，碳排放量将减少约32％。如果采用节能解决方案，则到2040年将达到12 MBOE /百万里亚尔。并且，到2040年，碳排放量将减少约32％。